arXiv subjects

Ryan E. Langendorf

Publications and source records attributed to Ryan E. Langendorf.

3 recordsLinked to original sources

arXiv2026

Optimal ambition in business, politics and life

In business, politics and life, folk wisdom encourages people to aim for above-average results, but to not let the perfect be the enemy of the good. Here, we mathematically formalize and extend this folk wisdom. We model a time-limited search for strategies having uncertain rewards. At each time step, the searcher either is satisfied with their current reward or continues searching. We prove that the optimal satisfaction threshold is both finite and strictly larger than the mean of available rewards -- matching the folk wisdom. This result is robust to search costs, unless they are high enough to prohibit all search. We show that being too ambitious has a higher expected cost than being too cautious. We show that the optimal satisfaction threshold increases if the search time is longer, or if the reward distribution is rugged (i.e., has low autocorrelation) or left-skewed. The skewness result reveals counterintuitive contrasts between optimal ambition and optimal risk taking. We show that using upward social comparison to assess the reward landscape substantially harms expected performance. We show how these insights can be applied qualitatively to real-world settings, using examples from entrepreneurship, economic policy, political campaigns, online dating and college admissions. We discuss implications of several possible extensions of our model, including intelligent search, reward landscape uncertainty and risk aversion.

physics.soc-ph↗

arXiv2021

Empirically Classifying Network Mechanisms

Network models are used to study interconnected systems across many physical, biological, and social disciplines. Such models often assume a particular network-generating mechanism, which when fit to data produces estimates of mechanism-specific parameters that describe how systems function. For instance, a social network model might assume new individuals connect to others with probability proportional to their number of pre-existing connections ('preferential attachment'), and then estimate the disparity in interactions between famous and obscure individuals with similar qualifications. However, without a means of testing the relevance of the assumed mechanism, conclusions from such models could be misleading. Here we introduce a simple empirical approach which can mechanistically classify arbitrary network data. Our approach compares empirical networks to model networks from a user-provided candidate set of mechanisms, and classifies each network--with high accuracy--as originating from either one of the mechanisms or none of them. We tested 373 empirical networks against five of the most widely studied network mechanisms and found that most (228) were unlike any of these mechanisms. This raises the possibility that some empirical networks arise from mixtures of mechanisms. We show that mixtures are often unidentifiable because different mixtures can produce functionally equivalent networks. In such systems, which are governed by multiple mechanisms, our approach can still accurately predict out-of-sample functional properties.

cs.SI↗

arXiv2019

Aligning Statistical Dynamics Captures Biological Network Functioning

Empirical studies of graphs have contributed enormously to our understanding of complex systems. Known today as network science, what was originally a theoretical study of graphs has grown into a more scientific exploration of communities spanning the physical, biological, and social. However, as the quantity and types of networks have grown so has their heterogeneity in quality and specificity. This has hampered efforts to develop general network theory capable of inferring functioning and predicting dynamics across study systems. We have successfully approached this challenge by aligning networks to each other rather than comparing parameter estimates from individually fitted models or properties of edge topologies. By comparing the predictability of statistical dynamics originating from each network's constituent nodes we were able to build a functional classifier that distinguished underlying processes in both synthetic and real-world network data spanning the entire biological scale from cellular machinery to ecosystems.

q-bio.QM↗